There are many known systems for recognising handwritten input to a pen-based computer system. Such systems normally entail recognising the individual characters in the input and converting these to ASCII text for subsequent storage and manipulation by the system. This involves a substantial computing overhead which increases with the recognition accuracy required. In known systems the recognition accuracy is rather limited for unconstrained input.
It is known in some handwriting recognition systems to locate the velocity minima in the input and to use this information to segment the input into strokes as a step in the recognition process.
Scribble matching has several advantages over full handwriting recognition followed by searching the resulting ASCII text:
i) for a personal device in which the search is comparing a user's input with previous input of the same user, the matching accuracy is higher. Handwriting recognition is currently limited in its ability to cope with the huge variety of letter forms which people employ when writing naturally. Scribble matching simply requires the user to be reasonably consistent in the letter forms which they use--it does not matter if that letter form is not recognisable per se; PA1 ii) for relatively small search lists of a few hundred entries of unconstrained writing, scribble matching is computationally cheaper than translation to ASCII text; PA1 iii) the writer is not restricted to a particular letter set but can use arbitrary symbols without any need to train the system. Small iconic pictures, personal symbols, symbols from other languages can all be used as long as they are sufficiently distinct and stable. PA1 locating the velocity minima in the freehand input; PA1 encoding the freehand input using a sequence of symbols each representing a feature of the freehand input at a velocity minimum; PA1 matching the sequence against the codes of other samples of freehand input using a string edit distance metric. PA1 cusp, open curve, closed curve and line end wherein the edit costs are dependent on the shape characteristics. PA1 coding the freehand input by a sequence of symbols which also represent features of points intermediate the velocity minima, wherein the intermediate points are generated by a re-sampling of the freehand input between velocity minima; PA1 encoding the velicity minima and the intermediate points by a triple containing the normalised height of the point, the angle to the tangent of the freehand input at the relevant point, and a classification of the point as a break in electronic ink, a velocity minimum or an intermediate point, PA1 and using edit costs dependent on the height, angle and point classification.
The following papers describe known scribble matching methods:
Daniel P. Lopresti and Andrew Tomkins. Pictographic naming. Technical Report 007-mei-pti-mitl-228-1, Matsushita Information Techology Laboratory, Princeton, November, 1992.
Daniel P. Lopresti and Andrew Tomkins. A comparison of techniques for graphical database queries. Technial Report MITL-TR45-93, Matsushita Information Techology Laboratory, Princeton, May, 1993.
Thierry Paquet and Yves Lecourtier. Recognition of handwritten sentences using a restricted lexicon. Pattern Recognition. 26(3):391-407, 1993.
The first two references relate to matching against pictograms which is a difficult problem due to significant within-writer variation. They describe the use of Hidden Markov modelling which is a statistical pattern recognition technique. The third reference describes matching offline ie static, scribble images which is a different problem from the one addressed by the present invention. The methods described in all three references give relatively poor accuracy.